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Authors: Seyed Hamid Khodadad Hosseini 1 ; Adel Azar 1 ; Ali Rajabzadeh Ghatari 1 and Arash Bahrammirzaee 2

Affiliations: 1 Tarbiat Modares University (TMU), Iran, Islamic Republic of ; 2 Iran Management and Productivity Center Researcher, Iran, Islamic Republic of

ISBN: 978-989-8425-84-3

Keyword(s): Energy forecasting, Neural network forecasting, Combined forecasting, Oil demand.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Modular Implementation of Artificial Neural Networks ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The purpose of this research is to study the combined forecasting methods in energy section. This method is a new approach which leads to considerable reduction of error in forecasting results. In this study, forecasting has been done through using individual methods (these methods consist of exponential smoothing methods, trend analysis, box-Jenkins, causal analysis, and neural network models) and also combining methods. In next step, the Results of these individual forecasting methods have been combined and compared with artificial neural networks, and multiple regression models. The data we used in this study are: dependent variable: OPEC oil demands from 1960 to 2005, and independent variables: oil price, GDP, other energy demands, population, and added-value in industry (in OECD countries. Computed indexes of errors are: MSE, MAPE, and GAPE which show considerable reductions in the errors of forecasting when using combining models. Therefore, it is suggested that the designed mo dels could be applied for oil demand forecasting. (More)

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Paper citation in several formats:
Khodadad Hosseini, S.; Azar, A.; Rajabzadeh Ghatari, A. and Bahrammirzaee, A. (2011). DEVELOPING COMBINED FORECASTING MODELS IN OIL INDUSTRY - A Case Study in Opec Oil Demand.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 205-210. DOI: 10.5220/0003681702050210

@conference{ncta11,
author={Seyed Hamid Khodadad Hosseini. and Adel Azar. and Ali Rajabzadeh Ghatari. and Arash Bahrammirzaee.},
title={DEVELOPING COMBINED FORECASTING MODELS IN OIL INDUSTRY - A Case Study in Opec Oil Demand},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={205-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003681702050210},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - DEVELOPING COMBINED FORECASTING MODELS IN OIL INDUSTRY - A Case Study in Opec Oil Demand
SN - 978-989-8425-84-3
AU - Khodadad Hosseini, S.
AU - Azar, A.
AU - Rajabzadeh Ghatari, A.
AU - Bahrammirzaee, A.
PY - 2011
SP - 205
EP - 210
DO - 10.5220/0003681702050210

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